Efficient reaching motion planning and execution for exploration by humanoid robots

This paper presents a reaching motion planning and execution framework tailored for exploration missions by human-operated humanoid robots in hazardous environments such as nuclear plants. This framework offers low-level but practical autonomy that allows the robot to plan and execute simple tasks, such as reaching a target object, within a reasonable amount of time. The human operator benefits from the efficiency of the framework to maneuver the robot without waiting for the planning results for minutes. The efficiency improvement is achieved in the following two phases. In the first phase, a reaching motion is planned quickly through approximation of mass distribution and kinematic structure to apply analytical solutions of inverse kinematics. Supposing that the robot is working in environments not completely known, the proposed planner can use measured voxel maps. In the second phase, the planned path is executed while compensating the approximation error in real time without violating other constraints. We confirm through simulations that a reaching motion for the HRP-2 humanoid with 30 DOFs in a constrained environment with pipes is planned in around one second. The simulation results also validate the efficiency of execution with real-time error compensation.